Abstract
With 98.4 million people diagnosed with diabetes in China, most of the Chinese health websites provide diabetes related news and articles in diabetes subsection for patients. However, most of the articles are uncategorized and without a clear topic or theme, resulting in time consuming information seeking experience. To address this issue, we propose an advanced deep learning approach to detect topics for diabetes related articles from health websites. Our research framework for topic detection on diabetes related articles in Chinese is the first one to incorporate deep learning in topic detection in Chinese. It can identify topics of diabetes articles with high performance and potentially assist health information seeking. To evaluate our framework, experiment is conducted on a test bed of 12,000 articles. The results showed the framework achieved an accuracy of 70% in detecting topics and significantly outperformed the SVM based approach.
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Chen, X., Zhang, Y., Xing, C., Liu, X., Chen, H. (2014). Diabetes-Related Topic Detection in Chinese Health Websites Using Deep Learning. In: Zheng, X., Zeng, D., Chen, H., Zhang, Y., Xing, C., Neill, D.B. (eds) Smart Health. ICSH 2014. Lecture Notes in Computer Science, vol 8549. Springer, Cham. https://doi.org/10.1007/978-3-319-08416-9_2
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DOI: https://doi.org/10.1007/978-3-319-08416-9_2
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